Browsing by Subject "Logistic regression"

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    Comprehensive and Computable Molecular Diagnostic Panel (C2Dx) From Small Volume Specimens for Precision Oncology: Molecular Subtyping of Non-Small Cell Lung Cancer From Fine Needle Aspirates
    (Frontiers Media, 2021-04-16) Su, Jing; Huang, Lynn S.; Barnard, Ryan; Parks, Graham; Cappellari, James; Bellinger, Christina; Dotson, Travis; Craddock, Lou; Prakash, Bharat; Hovda, Jonathan; Clark, Hollins; Petty, William Jeffrey; Pasche, Boris; Chan, Michael D.; Miller, Lance D.; Ruiz, Jimmy; Biostatistics, School of Public Health
    The Comprehensive, Computable NanoString Diagnostic gene panel (C2Dx) is a promising solution to address the need for a molecular pathological research and diagnostic tool for precision oncology utilizing small volume tumor specimens. We translate subtyping-related gene expression patterns of Non-Small Cell Lung Cancer (NSCLC) derived from public transcriptomic data which establish a highly robust and accurate subtyping system. The C2Dx demonstrates supreme performance on the NanoString platform using microgram-level FNA samples and has excellent portability to frozen tissues and RNA-Seq transcriptomic data. This workflow shows great potential for research and the clinical practice of cancer molecular diagnosis.
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    Large-Scale Data Mining to Optimize Patient-Centered Scheduling at Health Centers
    (Springer, 2018-09-04) Kunjan, Kislaya; Wu, Huanmei; Toscos, Tammy R.; Doebbeling, Bradley N.; BioHealth Informatics, School of Informatics and Computing
    Patient-centered appointment access is of critical importance at community health centers (CHCs) and its optimal implementation entails the use of advanced data analytics. This study seeks to optimize patient-centered appointment scheduling through data mining of Electronic Health Record/Practice Management (EHR/PM) systems. Data was collected from different EHR/PM systems in use at three CHCs across the state of Indiana and integrated into a multidimensional data warehouse. Data mining was performed using decision tree modeling, logistic regression, and visual analytics combined with n-gram modeling to derive critical influential factors that guide implementation of patient-centered open-access scheduling. The analysis showed that appointment adherence was significantly correlated with the time dimension of scheduling, with lead time for an appointment being the most significant predictor. Other variables in the time dimension such as time of the day and season were important predictors as were variables tied to patient demographic and clinical characteristics. Operationalizing the findings for selection of open-access hours led to a 16% drop in missed appointment rates at the interventional health center. The study uncovered the variability in factors affecting patient appointment adherence and associated open-access interventions in different health care settings. It also shed light on the reasons for same-day appointment through n-gram-based text mining. Optimizing open-access scheduling methods require ongoing monitoring and mining of large-scale appointment data to uncover significant appointment variables that impact schedule utilization. The study also highlights the need for greater "in-CHC" data analytic capabilities to re-design care delivery processes for improving access and efficiency.